Extracting High Quality Images from a Video

ABSTRACT

Various embodiments calculate a score for each frame of a video segment based on various subject-related factors associated with a subject (e.g., face or other object) captured in a frame relative to corresponding factors of the subject in other frames of the video segment. A highest-scoring frame from the video segment can then be extracted based on a comparison of the score of each frame of the video segment with the score of each other frame of the video segment, and the extracted frame can be transcoded as an image for display via a display device. The score calculation, extraction, and transcoding actions are performed automatically and without user intervention, which improves previous approaches that use a primarily manual, tedious, and time consuming approach.

BACKGROUND

Conventional image capture techniques used to capture a perfect momentis difficult with moving subjects or commotion in a scene. Because ofthis difficulty, some users have shifted to videography to record avideo of a long duration event and then manually sift through the videoto extract frames of interest. Using this lengthy manual process can bea useful alternative to candid photography, where capturing the perfectmoment requires near perfect timing and presence of mind on the part ofthe photographer.

Challenges arise when sharing videos versus images. Video files aregenerally larger files than image files, and require relatively morebandwidth and time to share via a network, such as by uploading to asocial media website. Thus, users in general tend to upload image filesto social media websites more frequently than video files. Althoughimage files are more easily and more quickly uploaded, it is theconstantly recording video that can capture fleeting moments commonlymissed when using a still camera that only takes photographs.

Capturing or otherwise obtaining still images of those fleeting momentsfor sharing via social media or other content-sharing platform ischallenging because conventional techniques for obtaining such stillimages are tedious and time consuming manual processes.

SUMMARY

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

Various embodiments calculate a score for each frame of a video segmentbased on various subject-related factors associated with a subject(e.g., face or other object) captured in a frame relative tocorresponding factors of the subject in other frames of the videosegment. A highest-scoring frame from the video segment can then beextracted based on a comparison of the score of each frame of the videosegment with the score of each other frame of the video segment, and theextracted frame can be transcoded as an image for display via a displaydevice.

The techniques described herein improve upon the traditional approachthat uses primarily manual, tedious, and time consuming techniques byautomatically and without user intervention identifying a best imagefrom a video segment. This enables a user to capture fleeting momentsthat are easily missed when using a still camera, and then quickly andeasily obtain a “best” image from each video segment.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures.

FIG. 1 is an illustration of a digital medium environment in an exampleimplementation that is operable to employ techniques described herein.

FIG. 2 illustrates an example image extraction module in accordance withone or more implementations.

FIG. 3 illustrates an example implementation of video segmentation inaccordance with one or more embodiments.

FIG. 4 illustrates an example implementation of a scoring moduleconfigured to score frames based on a zoom factor in accordance with oneor more embodiments.

FIG. 5 depicts a flow diagram of an example procedure for scoring framesbased on the zoom score algorithm in accordance with one or moreembodiments.

FIG. 6 illustrates an example implementation of the scoring moduleconfigured to score frames based on an alignment factor in accordancewith one or more embodiments.

FIG. 7 depicts a flow diagram of an example procedure for scoring framesbased on the alignment score algorithm in accordance with one or moreembodiments.

FIG. 8 illustrates an example implementation of the scoring moduleconfigured to score frames based on an eyes factor in accordance withone or more embodiments.

FIG. 9 depicts a flow diagram of an example procedure for scoring framesbased on an eyes score algorithm in accordance with one or moreembodiments.

FIG. 10 illustrates an example implementation of the scoring moduleconfigured to score frames based on an overlap factor in accordance withone or more embodiments.

FIG. 11 depicts a flow diagram of an example procedure for scoringframes based on an overlap score algorithm in accordance with one ormore embodiments

FIG. 12 illustrates and example implementation of the scoring moduleconfigured to score frames based on a motion factor in accordance withone or more embodiments.

FIG. 13 illustrates an example implementation of frame selection forextraction from a video segment based on associated scores.

FIG. 14 illustrates an example implementation of frame selection forextraction from a video segment based on associated scores.

FIG. 15 describes an example procedure for extracting high qualityimages from videos in accordance with one or more embodiments.

FIG. 16 illustrates an example system including various components of anexample device that can be employed for one or more searchimplementations described herein.

DETAILED DESCRIPTION

Overview

Capturing fleeting moments with a still camera is challenging, andrequires near perfect timing and presence of mind on the part of thephotographer. Capturing those same fleeting moments with a video is mucheasier, but video files are large and consume large amounts of bandwidthwhen transmitting via a network. Capturing the video and then extractingimages from the video to transmit via the network is not trivial, andgenerally requires a tedious and time consuming manual process ofbrowsing through the video frame by frame to find frames that are highquality, such as a well-focused and appropriately zoomed frame with thesubject's eyes open and facing the camera. Finding such a high qualityframe from among all the frames in the video is a burdensome andundesirable task using these conventional techniques.

Accordingly, techniques are described in the following for extractinghigh quality images from videos. These techniques identify subjects(e.g., faces and other image objects) in a video and their correlationwith each other and with the scene or background. The video is firstdivided into segments that are bounded by scene changes, or changes to anumber of faces or other objects in the scene. These segments are thenmerged to create merged segments that each contain a different type ofcontent than the other merged segments. Scores are then calculated foreach frame of a merged segment based on a variety of different factorsrelative to other frames in the video segment. Some example factorsinclude zoom, alignment of faces, open eyes, overlap of faces, motion,and orientation of faces. Other factors are also contemplated, such as anumber of faces or other objects, smile or emotion of each face, camerashake, blurriness, brightness, contrast, and so on. Each frame of themerged segment is assigned an overall score based on the scoresassociated with each factor. Then, a highest-scoring frame is extractedand transcoded as an image for display via a display device.

In an example implementation, a user capturing a video of a group ofpeople gathering together for a group shot can simply record the video.By recording the video, those perfect moments are captured when all thepeople in the group have their eyes open, are smiling, are alignedtogether, are not overlapping each other, and are well-focused.Subsequently, the video is analyzed using the techniques describedherein, and a high quality image for each different portion of the videois quickly and automatically presented to the user without requiringinput from the user. Using these techniques, the user can easily capturehigh quality images of fleeting moments that are ready and available toshare, such as by uploading to a social media site or by transmitting anemail or text to a friend.

As used herein, the term “object” (also referred to herein as “imageobject”) is representative of a material article that is depicted in animage, such as in a key frame of video data. The object can include anitem, a device, a gadget, an entity, a person, an animal, a plant, andso on. In implementations, the object can include a face of a persondepicted in an image. Thus, the term “object” can represent any of avariety of objects in images or in video data.

As used herein, the term “subject” refers to a primary object capturedin an image or video, such as the primary object on which a photographeror videographer focuses. The subject can include any of a variety ofdifferent objects, such as those described above captured in the imageor the video.

As used herein, the term “face change” refers to a change to a number offaces in a scene. For example, in a video including six people, one ormore of those people may exit the frame, or become hidden behind anobject such as another person in the frame. Thus, the face changerepresents a moment in the video when the number of faces in one framechanges to a different number of faces in a next frame. Similarly, theterm “object change” may refer to a change to a number of objects otherthan faces in the scene from one frame to the next frame. Further, theterm “scene change” may refer to a change to a background scenery. Forexample, in a video of people in standing together in a room, a scenechange may occur when the camera turns to record a different area of theroom, or may move to a different room. Alternatively, the scene can jumpto a different background setting, such as a garden, a driveway, or adifferent building with different people. In implementations, a blackscreen can indicate an ending to a scene and/or a beginning of a nextscene. Accordingly, the scene change represents a wide variety ofdifferent changes to background settings or scenery.

As used herein, the term “face segment” represents a segment of video(e.g., multiple consecutive frames) bounded on either side by a facechange. Similarly, the term “object segment” represents a segment ofvideo bounded on either side by an object change. Likewise, the term“scene segment” represents a segment of video bounded on either side bya scene change.

In the following discussion, an example digital medium environment isfirst described that can employ the techniques described herein. Exampleimplementation details and procedures are then described which can beperformed in the example digital medium environment as well as otherenvironments. Consequently, performance of the example procedures is notlimited to the example environment and the example environment is notlimited to performance of the example procedures. Finally, an examplesystem and device are described that are operable to use the techniquesand systems described herein in accordance with one or moreimplementations.

Example Digital Medium Environment

FIG. 1 is an illustration of a digital medium environment 100 in anexample implementation that is operable to utilize techniques forextracting high quality images from videos. As used herein, the term“digital medium environment” refers to the various computing devices andresources that can be utilized to implement the techniques describedherein. The illustrated digital medium environment 100 includes acomputing device 102 including a processing system 104 that includes oneor more processing devices, one or more computer-readable storage media106, and various applications 108 embodied on the computer-readablestorage media 106 and operable via the processing system 104 toimplement corresponding functionality described herein.

In at least some implementations, the applications 108 include orotherwise make use of an image extraction module 110. In someimplementations, the image extraction module 110 is a standaloneapplication. In other implementations, the image extraction module 110is included as part of another application or system software such as acomputing device's operating system. The image extraction module 110 isconfigured to automatically identify and extract high quality imagesfrom videos based on subject-related factors determined by applying oneor more sets of rules to various frames of the videos. The imageextraction module 110 is also configured to transcode extracted imagesfor display via a display device of the computing device as describedabove and below.

This constitutes an improvement over current approaches which use aprimarily manual approach to identify and select images for extraction.The automated nature of the described implementations provides a fast,efficient, and easily scalable solution, as discussed below in moredetail.

The computing device 102 can also include a video capture device 112that enables an end user to capture and record live-motion video andaudio for later playback. The video capture device 112 can include anyof a wide variety of devices, such as a camera phone, a digital camera,a webcam, and so on. Generally, the video capture device 112 recordsvideo in one of a variety of different formats, including MP4. In someimplementations, the video capture device 112 includes one or more ofthe applications 108, such as video editing applications, and/or theimage extraction module 110.

The computing device 102 may be configured as any suitable type ofcomputing device. For example, the computing device may be configured asa desktop computer, a laptop computer, a television, a mobile device(e.g., assuming a handheld configuration such as a tablet or mobilephone), a tablet, a camera, a portable video camera recorder(“camcorder”), and so forth. Additionally, although a single computingdevice 102 is shown, the computing device 102 may be representative of aplurality of different devices to perform operations “over the cloud” asfurther described in relation to FIG. 14.

The digital medium environment 100 further depicts one or more serviceprovider systems 114, configured to communicate with the computingdevice 102 over a network 116, such as the Internet, to provide a“cloud-based” computing environment. Generally speaking, a serviceprovider system 114 is configured to make various resources 118available over the network 116 to clients. In some scenarios, users maysign up for accounts that are utilized to access correspondingresources, such as images or video, from a provider. The provider mayauthenticate credentials of a user (e.g., username and password) beforegranting access to an account and corresponding resources 118. Otherresources 118 may be made freely available, (e.g., withoutauthentication or account-based access). The resources 118 can includeany suitable combination of services and/or content typically madeavailable over a network by one or more providers. Some examples ofservices include, but are not limited to, a social networking service, amessaging service, and so forth. Content may include variouscombinations of assets, video comprising part of an asset, audio,multi-media streams, animations, images, web documents, web pages,applications, device applications, and the like.

Although the network 116 is illustrated as the Internet, the network mayassume a wide variety of configurations. For example, the network 116may include a wide area network (WAN), a local area network (LAN), awireless network, a public telephone network, an intranet, and so on.Further, although a single network 116 is shown, the network 116 may berepresentative of multiple networks.

Various types of input devices and input instrumentalities can be usedto provide input to computing device 102. For example, the computingdevice can recognize input as being a mouse input, stylus input, touchinput, input provided through a natural user interface, keyboard input,button actuation input, and the like. Thus, the computing device canrecognize multiple types of gestures including touch gestures andgestures provided through a natural user interface.

Example Image Extraction Module

FIG. 2 illustrates an example image extraction module 110 in accordancewith one or more implementations. The image extraction module 110 isillustrated as including a segmentation module 202, a scoring module204, and a selection module 206. These modules can be implemented in anysuitable hardware, software, firmware, or combination thereof.

The segmentation module 202 is representative of functionality, andconstitutes but one means, that analyzes a video 208, such as a videofile, and divides the video 208 into a plurality of video segments 210.In some implementations, the segmentation module 202 divides the video208 into video segments 210 based on one or more scene changes.Alternatively, the video 208 can be divided based on object changes inthe video, such as a change in a number of objects. Further, the video208 can be divided more particularly based on face changes in the video,such as a change in a number of faces. In at least some implementations,the segmentation module 202 utilizes a combination of scene changes,object changes, and/or face changes to divide the video 208 into thevideo segments 210.

The scoring module 204 represents functionality to apply a set of rulesto each frame of each video segment 210 to calculate, for each frame, aset of scores corresponding to different factors 212. Some examplefactors 212 include zoom 214, alignment 216, eyes 218, overlap 220,motion 222, and orientation 224, each of which are described below inmore detail. Using the set of scores, the scoring module 204 assigns anoverall score to each frame, where the overall score represents anoverall level of image quality of the frame based on the factors 212.

The zoom 214 factor indicates whether a magnification level of objects(e.g., faces), is changed from a frame to a next frame, such as toexclude one or more of the objects or to include one or more additionalobjects. The alignment 216 factor represents a measure of a distancebetween the objects in the frame. If the frame includes faces, the eyes218 factor indicates whether the eyes of those faces are open. Theoverlap 220 factor represents a measure of objects overlapping one ormore other objects in the frame. The motion 222 factor represents alevel of activity occurring in the frame, which indicates a an amount ofimage blur. The orientation 224 factor indicates a measure of divergencebetween respective orientations of objects in the frame. Additionalfactors that can be calculated and used to assign corresponding scoresinclude factors based on area occupied by objects in the frame, thenumber of objects in the frame, an emotion or smile of each face in theframe, camera shake, blurriness, brightness and contrast, and so on.Accordingly, any of a variety of different factors can be detected,analyzed, and used to assign scores to each frame of the video segment210. These and other factors are described in further detail below.

After the scoring module 204 generates scores for the frames in eachvideo segment 210, the selection module 206 analyzes the scores 226 toidentify and select a highest-scoring frame in each video segment 210.The highest-scoring frame in each video segment 210 is then transcodedas an output image 228 for display via a display device.

FIG. 3 illustrates an example implementation 300 of video segmentationin accordance with one or more embodiments. In the illustratedimplementation 300, the segmentation module 202 analyzes the video 208to identify and locate scene changes. The segmentation module 202 thendivides the video 208 into segments using the scene changes asboundaries for each segment. In this way, the segmentation module 202generates scene segments 302 representing video segments bounded byscene changes.

Using object detection techniques, object information indicating anumber of objects in each frame is determined by the segmentation module202. The object information is then used to identify and locate frameswhere the number of objects changes relative to a previous or nextframe. These located frames can be used as boundaries to divide thevideo 208 into object segments 304 representing video segments boundedby object changes. This process can be repeated using more specific facedetection techniques, such as facial recognition systems, to obtain faceinformation indicating a number of faces in each frame. Using the faceinformation, the segmentation module 202 identifies and locates frameswhere the number of faces changes relative to a previous or next frame.Then, the segmentation module 202 generates face segments 306representing video segments bounded by face changes.

In at least some implementations, the segmentation module 202 determineswhich frames to use as boundaries for each of the scene segments 302,object segments 304, and face segments 306 without generating copies ofthe video 208. Rather, the segmentation module 202 identifies the framesfor the different boundaries and uses this information to analyzedifferent groups of frames according to a particular type of boundary,e.g., scene, object, or face boundary. Subsequently, the segmentationmodule 202 merges the scene segments 302, object segments 304, and facesegments 306 to provide a set of merged segments 308 that each includedifferent content in comparison to one another. The set of mergedsegments 308 is representative of the video segments 210 in FIG. 2.

The scoring module 204 evaluates each of the merged segments 308, suchas merged segment 310 having a plurality of frames 312, and analyzes theframes 312 of the merged segment 310 relative to one another. Then, thescoring module 204 calculates and assigns scores for each individualframe of the merged segment 310. For example, the scoring module 204 candetermine scores for frame k 314 and frame k+1 316 based on a number ofdifferent factors, such as the factors 212 described in FIG. 2. Eachscore for frame k 314 represents a different factor 212 relative to acorresponding factor in frame k+1 316. The scoring module 204 thencalculates an overall score for each frame in the merged segment 310.The overall score includes a value representing an overall level ofimage quality of the frame relative to the other frames in the mergedsegment 310 based on the scores of the factors 212 for the frame. Theoverall score includes a weighted mean of the scores of the factors 212.The weights can initially be equal, but machine learning can be utilizedto learn and improvise the weights. Also, a user can customize theweights based on one or more requirements. Based on an overall score foreach frame, a highest-scoring frame is selected, extracted, andtranscoded as an image.

As mentioned above, the scores for each frame are based on a variety ofdifferent factors 212 that are related to the subject (e.g., person orother image object) captured in each frame. Consider now a discussion ofthe example factors 212 and techniques for determining scores based oneach factor 212 in relation to FIGS. 4-10. In the discussion below,various example algorithms are discussed in relation to faces identifiedin the video frames. This is not to be construed as limiting, and theexample algorithms discussed below can also be applied to any of avariety of different objects identified in the frames.

For purposes of the discussion below, let each frame number k in mergedsegment M_(i) be denoted as Fr_(k). For each frame Fr_(k), let the facesin the frame Fr_(k) be denoted by f_(yk), where y is a valuerepresenting a particular face. Further, let the center of each of thefaces f_(yk), in the frame Fr_(k) be denoted as c_(yk). Additionally,let the area of each face f_(yk) be denoted as a_(yk).

FIG. 4 illustrates an example implementation 400 of the scoring module204 scoring frames based on the zoom 214 factor in accordance with oneor more embodiments. In an example scenario, a videographer may zoom inor zoom out over faces in an attempt to capture the best shot possible.In this example scenario, the videographer may continue zooming in orout until he is satisfied that the best shot has been captured. Based onthis, the scoring module 204 determines whether zooming in or out isoccurring on faces in the merged segment 310.

Below is an example zoom score algorithm applied by the scoring module204 to calculate a zoom-in score for each frame in the merged segment:

-   -   i. Iterate on each frame Fr_(k) of the merged segment M_(i).        Start with frame k.    -   ii. Consider two consecutive frames Fr_(k) and Fr_(k+1).    -   iii. Check if:        -   a. Number of faces in both frames Fr_(k) and Fr_(k+1) are            the same        -   b. For each face f_(yk) in Fr_(k) find corresponding face            f_(y(k+1)) in Fr_(k+1) such that the centers c_(yk) of these            two faces are at approximately same relative position.        -   c. The area of each face f_(yk) in Fr_(k) is less than its            corresponding face f_(y(k+1)) in Fr_(k+1).    -   iv. If all conditions in step iii are satisfied, then repeat        step iii for at least next two consecutive frames (e.g., frames        Fr_(k+1) and Fr_(k+2)) until reaching a frame Fr_(p) for which        the conditions in step iii are not satisfied. Then, the frames        from Fr_(k) to Fr_(p) are determined to have an increasing level        of magnification (e.g., zoom in).    -   v. Repeat steps i-iv for remaining segments M_(i+1) through        M_(i+n).

In the example zoom score algorithm above, the frame Fr_(p) indicates anend boundary for a group of frames from Fr_(k) to Fr_(p). In this groupof frames, the frame Fr_(p) is assigned the highest relative zoom-inscore because the zoom 214 is at its maximum relative to the otherframes in the group, where the number of faces is the same in the framesfrom Fr_(k) to Fr_(p), and the centers of the faces in frame of thegroup are each at approximately the same relative position. A similarprocess is performed for calculating a zoom-out score for each frame inthe merged segment 310, except that step iii(c) checks if the areaa_(yk) of each face f_(yk) is greater than the area a_(y(k+1)) itscorresponding face f_(y(k+1)) in Fr_(k+1).

In the illustrated example, the merged segment 310 includes frames 402,404, 406, and 408, which represent frames Fr_(k), Fr_(k+1), Fr_(p), andFr_(p+1), respectively. The frame 402 includes two subjects facing eachother. Applying the above set of rules to the frame 402 and the nextframe 404 in the segment yields results indicating that the camera iszooming in to the subjects because the area of face 410 a in frame 402is less than the area of the corresponding face 410 b in the next frame404. In addition, the next frame 404 includes the same number of facesas that of the frame 402. Consequently, the next frame 404 is assigned arelatively higher zoom score in comparison to the frame 402. Consecutiveframes in the merged segment 310 are analyzed in this manner untilreaching a frame p, e.g., frame 406. When the scoring module 204 appliesthe above rules to frame 406 and subsequent frame 408, the conditions instep iii are not all satisfied. For instance, the frame 406 does notinclude the same number of faces as the frame 408 because the camera haszoomed in on the subjects beyond the point at which both subjects canremain in the frame. For example, the frame 406 includes both face 410 cand 412 a. However, in the frame 408, the camera has zoomed in to face410 d effective to cut at least a portion of the other face 412 b out ofthe frame 408. In this implementation, frame 406 is assigned the highestzoom score relative to the other frames 402, 404, and 408 in the mergedsegment 310.

Use of the rule sets constitutes an improvement over current approachesthat use a primarily manual approach to identify and extract images fromvideo. The automated nature of the described implementations provides afast, efficient, and easily scalable solution. That is, through the useof automated rules of the particular types described herein, highquality images can be more quickly and efficiently identified andextracted from video. Moreover, the automated rules promote scalabilityby removing the need for human intervention, such as additional humansto perform an arduous manual process.

FIG. 5 depicts a flow diagram of an example procedure 500 for scoringframes based on the zoom score algorithm in accordance with one or moreembodiments. Aspects of the procedure may be implemented in hardware,firmware, or software, or a combination thereof. The procedures areshown as a set of blocks that specify operations performed by one ormore devices and are not necessarily limited to the orders shown forperforming the operations by the respective blocks. In at least someimplementations the procedures may be performed in a digital mediumenvironment by a suitably configured device, such as the examplecomputing device 102 described with respect to FIG. 1.

At 502, a number of faces are detected in an initial frame of a videosegment having a sequence of frames. At 504, a number of faces in a nextframe of the video segment is detected. These operations can beperformed in any suitable way. For example, in one or moreimplementations, a face detection engine can be utilized to identify andlocate faces of subjects in the initial and next frames of the videosegment. The face detection engine provides information usable todetermine an area of each face, a center of each face, and a location ofthe center of each face within the frame. The information detected bythe face detection engine can also include relative position, sizeand/or shape of the eyes, nose, cheekbones, and jaw. Accordingly, avariety of distinctive features of a face can be detected by the facedetection engine

At 506, the initial frame and the next frame are compared. For example,at 508, a determination is made as to whether the initial frame and thenext frame have the same number of faces. If the next frame does nothave the same number of frames as the initial frame, then the comparisonends at 510 and the initial frame is assigned the highest relative scorefor the zoom 214 factor for the video segment. If, however, the numberof faces is the same in both the initial frame and the next frame (e.g.,“YES”), then at 512 a determination is made as to whether the center ofeach face in the initial frame is at a same approximate relativeposition as the center of each corresponding face in the next frame. Ifthe center of a face in the initial frame is located at a substantiallydifferent location than the center of a corresponding face in the nextframe, then the comparison ends at 510 and the initial frame is assignedthe highest relative score for the video segment. If, however, thecenters of corresponding faces in the initial frame and the next frameare located at similar relative positions, then the comparison continuesto 514.

At 514, a determination is made as to whether an area of each face inthe initial frame is less than (for zooming in), or greater than (forzooming in), the area of each corresponding face in the next frame. Forexample, if a face in the initial frame has a relatively smaller areacompared with that of the same face in the next frame, such that theface in the next frame appears larger in comparison to the face in theinitial frame, then it is determined that the video is zooming in.Alternatively, if the face in the initial frame has a relatively largerarea than that of the corresponding face in the next frame, then it isdetermined that the video is zooming out. If the respective areas arethe same, however, then it is determined that zooming has ceased (e.g.,“NO”). If zooming has ceased, then the comparison ends and at 510 theinitial frame is assigned the highest relative score for the videosegment.

If conditions are satisfied at 514 (e.g., “YES”), then at 516 the nextframe is assigned a relatively higher score than the initial frame. Thishigher score indicates a higher level of zoom in (or zoom out) incomparison to the initial frame. Then, at 518, the next frame is treatedas a new initial frame and compared with a next consecutive frame in thevideo segment. This process continues to analyze and compare each pairof consecutive frames in the video segment until one or more of theconditions described in 508-510 are not satisfied. Each frame in thevideo segment is assigned a different score based on the zoom factor,also referred to as a zoom score.

FIG. 6 illustrates an example implementation 600 of the scoring module204 scoring frames based on the alignment 216 factor in accordance withone or more embodiments. In an example scenario, a video capturesmultiple subjects walk ing towards each other from different parts ofthe frame area and coming together for some seconds near one another toform a group for a group picture. The scoring module 204 analyzes thevideo to automatically identify this type of scenario and assigns scoresto frames based on the alignment 216 of the subjects in the frame. Belowis an example alignment score algorithm applied by the scoring module204 to calculate an alignment score for each frame in the merged segment310 based on the subjects aligning together:

-   -   i. Iterate on each frame Fr_(k) of the merged segment M_(i).        Start with frame k.    -   ii. Consider two consecutive frames Fr_(k) and Fr_(k+1).    -   iii. Check if number of faces in both frames Fr_(k) and Fr_(k+1)        are the same. If the number of faces are the same, then        continue, else go to start and begin from next frame.    -   iv. For frame Fr_(k), find variance Vr_(k) based on the center        c_(yk) of each face f_(yk).    -   v. For frame Fr_(k+1), find variance Vr_(k+1) based on the        center c_(y(k+1)) of each face f_(y(k+1)).    -   vi. Check if Vr_(k)>Vr_(k+1)    -   vii. If condition in step vi is satisfied, then move further        until finding a frame p for which Vr_(p−1)˜Vr_(p) and it is        stable at this value for at least next two frames until frame q,        where Vr_(q)<Vr_(q+1), so region ends at Vr_(q).    -   viii. Assign frames in region beginning at Fr_(p) and ending at        Fr_(q) a highest relative score for the alignment factor in the        merged segment M_(i). Other frames previous to frame p and after        frame q are assigned a lower relative score for the alignment        factor.    -   ix. Repeat steps i-viii for remaining segments M_(i+1) through        M_(i+n).

In the example alignment score algorithm above, the variance representsdistances between the centers of the faces in the frame. In addition,the frame Fr_(q) represents an end boundary for a region of frames fromFr_(p) to Fr_(q). In this region of frames, the frames from Fr_(p) toFr_(q) are assigned the highest relative alignment score because thoseframes include the subjects aligned together for the group shot. Thevariance is lowest when the subjects are near one another for the groupshot. Thus, lower variance frames are assigned higher alignment scores.

In the illustrated example, the merged segment 310 includes threesubjects moving towards one another, settling into a group for a periodof time (e.g., several frames of the video segment), and then movingaway from one another. Example frames 602, 604, 606, and 608 areillustrated as sample frames from the merged segment 310. The varianceof frame 602 is based on distances between the centers of the faces inthe frame 602, such as distance 610 a between center 612 of face 614 andcenter 616 of face 618. The variance also incorporates distance 620 abetween the center 616 of face 618 and center 622 of face 624. Thevariance of frame 602 is compared with the variance of the next frame604 to determine whether the subjects (e.g., faces) are gatheringtogether. In the illustrated example, the distances 610 a, 620 a inframe 602 are relatively greater than corresponding distances 610 b, 620b in frame 604. The variance can represent the distances between thecenters of all the faces in the frame. Here, the variance of the frame604 is lower than the variance of the frame 602.

In the illustrated example, the subjects hold their approximatepositions from frame 604 to frame 606, and this is determined becauserespective variances of the frames between and including frame 604 andframe 606 are approximately the same. Notice, however, that the subjectsin frame 608 are moving away from each other because the group shot hasfinished. Because of this movement, the variance of frame 608 isrelatively larger than the variance of frame 606, and this difference inrelative variance is used as an indication of an end boundary of theregion of frames having the group shot. Accordingly, the frames in theregion from frame 604 to frame 606 are assigned higher scores incomparison to other frames in the merged segment 310.

FIG. 7 depicts a flow diagram of an example procedure 500 for scoringframes based on the alignment score algorithm in accordance with one ormore embodiments. Aspects of the procedure may be implemented inhardware, firmware, or software, or a combination thereof. Theprocedures are shown as a set of blocks that specify operationsperformed by one or more devices and are not necessarily limited to theorders shown for performing the operations by the respective blocks. Inat least some implementations the procedures may be performed in adigital medium environment by a suitably configured device, such as theexample computing device 102 described with respect to FIG. 1.

At 702, an initial frame is compared with a next frame in a sequence offrames in a video segment. At 704, it is determined by the scoringmodule 204 whether a number of faces in both the initial frame and thenext frame are the same. If the number of faces is not the same (e.g.,“NO”), then the next frame is treated as a new initial frame and theprocess restarts at 702 to compare the new initial frame with a new nextframe. If the number of faces is the same in both the initial frame andthe next frame (e.g., “YES”), then at 708 a variance is determined foreach of the initial and next frames based on distances between thecenters of each face. At 710, the scoring module 204 determines whetherthe variance of the initial frame is greater than the variance of thenext frame. If so (e.g., “YES”), then at 714 the next frame is assigneda relatively higher score than the initial frame, at 706 the next frameis treated as the new initial frame, and the process continues at 702 bycomparing the new initial frame with a new next frame. If the varianceof the initial frame is not greater than the variance of the next frame(e.g., “NO”), then at 712 the initial frame is assigned a highestrelative alignment score for the video segment and the process ends. Inthis way, each frame in the video segment may be assigned a differentalignment score based on the alignment factor.

FIG. 8 illustrates an example implementation 800 of the scoring module204 scoring frames based on the eyes 218 factor in accordance with oneor more embodiments. Capturing group photos can be challenging becausedifferent people blink their eyes at different rates. Consequently, manyframes of captured video can include one or more persons having theireyes open while another person's eyes are closed or partially open. Aphotographer generally captures multiple photos of a group of people inan attempt to capture at least one shot with everyone's eyes open. Thiscan be frustrating for the photographer and also for the people beingphotographed.

In the illustrated implementation 800, the merged segment 310 includestwo people facing one another. In frame 802, face 804 a and face 806 aboth have eyes open. In frame 808, face 804 b has eyes closed because heis blinking, while face 806 b has eyes open. In frame 810, face 804 chas eyes open while face 806 b has eyes partially open. In this example,the scoring module 204 assigns frame 802 a relatively higher score thanframes 808, 810 because both faces 804 a, 806 a have their eyescompletely open. The frames 808, 810 are assigned relatively lowerscores because of at least one face in each frame that has eyes closedor only partially open.

The image extraction module 110 determines, for each frame of a mergedsegment, whether the eyes of each subject in the frame are open, closed,or partially open. Eye information is then analyzed by the scoringmodule 204 to assign scores to each frame. An example eyes scorealgorithm applied by the scoring module 204 includes:

-   -   i. Iterate on each frame of the merged segment, starting with        frame k    -   ii. calculate for Fr_(k)=(number of faces whose eyes are open in        Fr_(k)+(0.5*number of faces whose eyes are partially open in        Fr_(k)))/total number of faces in Fr_(k).

The scoring module 204 applies the above eyes score algorithm to eachframe in the merged segment, and assigns a resultant score to eachframe. In this way, frames having eyes open are more likely to beselected for extraction.

FIG. 9 depicts a flow diagram of an example procedure 900 for scoringframes based on the eyes score algorithm in accordance with one or moreembodiments. At 902, a first number of faces in the frame with eyes openis determined. For example, a facial recognition system can identifyfacial features by extracting various landmarks, or features, of asubject's face in an image, such as a video frame. The landmarks caninclude relative position, size and shape of the eyes of the face. Usingthese landmarks, the computing device can determine whether the eyes ofa particular face are open, partially, open or closed, which in turn canbe used to determine the number of faces with eyes open in the frame. At904, a second number of faces in the frame with eyes partially open isdetermined. This second number of faces can be determined using thelandmarks detected by the facial recognition system. At 906, a totalnumber of faces in the frame is determined.

At 908, an eyes score is calculated for the frame based on the firstnumber of faces with eyes open, the second number of faces with eyespartially open, and the total number of faces in the frame. Inimplementations, the eyes score can be calculated using the eyes scorealgorithm described above. Then, at 910, the eyes score is assigned tothe frame to represent a level of quality of the frame based on the eyesin the frame. In this way, frames with a relatively higher eyes scoreare more likely to be selected for extraction from the video segmentbecause such frames have relatively more subjects with eyes open, whichis generally more desirable by users, particularly when capturing agroup shot.

FIG. 10 illustrates an example implementation 1000 of the scoring module204 scoring frames based on the overlap 220 factor in accordance withone or more embodiments. Because subjects in the video can move aboutduring a video shoot, some scenarios include one or more faces being atleast partially overlapped by another face or object. Frames having aface with partial or full overlap may not be desirable candidates forextraction because the overlapped face is obscured and not visible inthe frame. In the illustrated example, frame 1002 includes three peoplewith faces, but a face 1004 a is overlapping (e.g., obscuring) a face1006 a such that the face 1006 a is hidden from view. Thus, frame 1002may not be desirable and is thus assigned lower relative score. In frame1008, the face 1004 b is less obscured by the face 1006 b, but the face1004 b is only partially viewable, which is also not desirable. In frame1010, however, the face 1004 c is no longer being overlapped by face1006 c or any other face or object, and all faces in the frame 1010 areviewable. Thus, the scoring module 204 can assign frame 1010 a highestrelative overlap score in comparison to the other frames 1002, 1008 inthe merged segment 310 because the overlap of faces is minimized.

Facial overlap is automatically detected by the image extraction module110 in each frame of a video segment. For partial overlap, a facedetection engine is used to detect each face in the frame. Then, theimage extraction module 110 detects whether the area of any face in theframe overlaps any other facial area in the frame. Using the areas offaces in the frames, partial overlap can be detected. In full overlap,however, the overlapped face is substantially hidden by another face,and is not likely to be detected by the face detection engine. In thisscenario, both faces and their respective directions are tracked over apredefined number of previous frames and a predefined number ofsubsequent frames. Full overlap is detected if:

-   -   i. In the predefined number of previous frames, both faces        generally create partial overlap cases and their area of overlap        is increasing frame by frame until Fr_(k)        -   OR    -   ii. In the predefined number of subsequent frames, both faces        generally create partial overlap cases and their area of overlap        is decreasing frame by frame as preceding away from Fr_(k).

After detecting the number of partial overlaps and the number of fulloverlaps in frame Fr_(k), then the overlap score for the frame iscalculated as follows:

-   -   iii. ((Number of faces with no overlap+(0.5*Number of faces        which are partially overlapped))/(Total number of faces in the        frame Fr_(k) (including the face(s) which are fully overlapped))

In this way, each frame is assigned a relative overlap score by thescoring module 204 based on whether the frame includes overlapped faces,some of which are detected relative to previous and/or subsequent framesin the merged segment.

FIG. 11 depicts a flow diagram of an example procedure 1100 for scoringframes based on the overlap score algorithm in accordance with one ormore embodiments. At 1102, a first number of partially overlapped facesin the frame is detected by determining whether an area of a first faceoverlaps a facial area of a second face. As discussed above, this firstnumber is determined based on data obtained using the facial recognitionsystem, which provides an area and a relative position of each face. At1104, a second number of fully overlapped faces is detected in theframe. This second number is determined based on 1106 or 1108.

At 1106, an area of partial overlap between two faces is tracked over apredefined number of previous frames to determine whether the area ofoverlap is increasing frame by frame until reaching the video frame. Ifthe area of overlap is increasing frame by frame as approaching thevideo frame, then the increasing area indicates one of the two faces isfully overlapped by the other of the two faces, and the overlapped faceis moving behind the overlapping face in the video segment. If, however,the area of overlap is not increasing frame by frame as approaching thevideo frame, then there may not be an overlapped face at that locationin the video frame.

At 1108, the area of the partial overlap is tracked to determine whetherthe area is decreasing frame by frame as preceding away from the videoframe. If the area of overlap is decreasing frame by frame as precedingaway from the video frame, then the decreasing area of overlap indicatesthat one of the two faces is fully overlapped in the video frame, andthe overlapped face is emerging from behind the overlapping face as thevideo segment continues. If, however, the area of overlap is notdecreasing frame by frame as preceding away from the video frame, thenthere may not be an overlapped face at that location in the video frame.

At 1110, a third number of faces with no overlap in the frame isdetermined. At 1112, an overlap score is calculated for the video framebased on the third number of faces with no overlap, the first number ofpartially overlapped faces, and a total number of faces including fullyoverlapped faces in the frame. Then, at 1114, the overlap score isassigned to the video frame to represent a level of quality of the videoframe based on whether any of the faces in the video frame are at leastpartially hidden.

FIG. 12 illustrates and example implementation 1200 of the scoringmodule 204 scoring frames based on the motion 222 factor in accordancewith one or more embodiments. In at least some scenarios, a videosegment having fast moving subjects can appear clear during playback ofthe video but individual frames can appear blurry or distorted. Thus,scoring the frames based on the motion 222 factor can improve theautomatic image extraction technique by avoiding blurry frames.

In the illustrated example, frame 1202 is determined to be too blurrybecause of a high level of motion associated with the subjects ridingbicycles in the frame 1202. Thus frame 1202 is assigned a low motionscore and is thus rejected as a candidate for extraction. In addition,frame 1204 is also detected as including a high level of motion, and isthus rejected as a candidate for extraction because the level of blur ishigh. Frame 1206, however, has less blur and higher focus in comparisonto frames 1202, 1204 because the level of motion is relatively lower.Thus, frame 1206 is selected by the selection module 206 as a candidatefor extraction from the video.

To determine the motion score for each frame, the scoring module 204categorizes different portions of the merged segment 310 based ondifferent activity level of the content in the merged segment 310. Forexample, motion is detected in each frame of the merged segment 310 byusing a point tracker algorithm. Any suitable point tracker algorithmcan be utilized. The motion is segmented into different portions basedon one or more threshold values, which are predefined values and can beimproved with machine learning and user input. In at least oneimplementation, the different portions include a high activity portion,a medium activity portion, and a low activity portion. If the motion ina particular frame is low (e.g., the particular frame lies within a lowactivity portion), then it is highly likely that the particular frameincludes a low level of blur and/or a high level of focus. If, forexample, the motion in a portion of the merged segment 310 is high(e.g., camera is shaking, subject is running), then each frame in thatportion is likely to have a relatively higher level of blur ordistortion, which is not desirable.

Accordingly, the scoring module 204 assigns a motion score to each frameof the merged segment 310 based on a corresponding activity portion inwhich the frame is located. For example, if a frame is located within ahigh activity portion of the merged segment 310, then the frame isassigned a motion score of ⅓. Frames located within a medium activityportion are assigned a motion score of ⅔. Additionally, frames locatedwithin a low activity portion are scored with a one (1). Any number ofadditional activity portions can be utilized to further segment thelevel of motion in the merged segment 310 and refine the correspondingmotion scores.

FIG. 13 illustrates an example implementation 1300 of frame selectionfor extraction from a video segment based on associated scores. Forexample, the example implementation 1300 includes sample frames 1302,1304, 1306, and 1308 taken from a video segment of children playing on aplayset. Frame 1302 is an example of a frame that has low face count incomparison to other frames in the video segment, and is determined to betoo blurry based on a high level of motion in the frame 1302. Because ofat least these factors, frame 1302 is rejected. Frame 1304 includes amaximum number of faces detected with little overlap, and a lowblurriness based on a low level of motion. Thus, the frame 1304 isselected by the selection module 206 as a candidate for extraction.Frame 1306 includes a face count of three with a partial overlap offaces 1310, 1312, which results in the frame 1306 being rejected. Inaddition, frame 1308 includes a face count of two, which is less thanother frames in the video segment, and also includes partial overlaps offaces 1314, 1312. Frame 1308 is zoomed in on face 1316 in front, but isrejected because of the other factors that result in low scores for theframe 1308.

FIG. 14 illustrates an example implementation 1400 of frame selectionfor extraction from a video segment based on associated scores. Forexample, the example implementation 1400 includes sample frames 1402,1404, 1406, and 1408 taken from a group of people at a social gathering.Frame 1402 is zoomed out to include an entire group of people, butvisibility of individual faces is low. Thus, the frame 1402 is rejected.Frame 1404 is zoomed in on faces 1410, 1412, but excludes other facesfrom the group. Because the face count is low in comparison to the otherframes in the video segment, the frame 1404 is rejected. Frame 1406 isrejected because, although it is zoomed out to capture the entire group,the visibility of the faces is low and face 1410 is overlapped such thatit is completely hidden.

Frame 1408 includes a maximum face count, less blurriness, and lessdivergence, in comparison to the other frames. The divergence refers todifferences in orientations of each face in the frame. A low divergenceindicates that all the faces in the frame are facing a similardirection, such as toward the camera, which is generally desirable ingroup shots. A high divergence indicates that different faces are facingin different directions. Such a scenario may be desirable when thesubjects are posing in particular positions. For example, one person'sface or head may be facing upwards while another person's face or headis facing downwards. In implementations, a divergence above a predefinedvalue can indicate that the people are intentionally standing inparticular positions for the group shot. Accordingly, the frame isassigned an orientation score by the scoring module 204 based on theorientation 224 or the divergence in orientation 224. In addition, theframe 1408 includes an appropriate zoom level, and the faces in theframe 1408 are smiling and have eyes open. Accordingly, the frame 1408is selected by the selection module 206 as a candidate for extractionfrom the video.

Example Procedure

The following discussion describes techniques for extracting highquality candid images from videos that may be implemented utilizing thepreviously described systems and devices. Generally, any of thecomponents, modules, methods, and operations described herein can beimplemented using hardware (e.g., fixed logic circuitry), firmware,software, or any combination thereof. Some operations of the examplemethods may be described in the general context of executableinstructions stored on computer-readable storage memory that is localand/or remote to a computer processing system, and implementations caninclude software applications, programs, functions, and the like.Alternatively or in addition, any of the functionality described hereincan be performed, at least in part, by one or more hardware logiccomponents, such as, and without limitation, Field-programmable GateArrays (FPGAs), Application-specific Integrated Circuits (ASICs),Application-specific Standard Products (ASSPs), System-on-a-chip systems(SoCs), Complex Programmable Logic Devices (CPLDs), and the like.

FIG. 15 describes an example procedure 1500 for an improved imageextraction method to extract high quality images from videos. Theprocedures are shown as a set of blocks that specify operationsperformed by one or more devices and are not necessarily limited to theorders shown for performing the operations by the respective blocks. Inat least some implementations the procedures may be performed in adigital medium environment by a suitably configured device, such as theexample computing device 102 of FIG. 1.

At 1502, a video is segmented into a plurality of video segments basedon at least one of a scene change or a face change. This can beperformed in any suitable way, examples of which are described above.Each video segment is then analyzed to identify one frame within thevideo segment to extract and transcode as a high quality image.

At 1504, a score is calculated for each frame of the video segment basedon a variety of different subject-related factors associated withsubjects captured in the frame relative to corresponding factors of thesubjects captured in other frames of the video segment. Thesubject-related factors include a variety of factors associated with thesubject, such as a level of zoom on the subject, alignment of multiplesubjects in the frame, whether the subject's eyes are open, overlap ofone subject over another, a level of motion within the framecorresponding to a level of blur, divergence of orientation of faces inthe frame, whether the faces are smiling, and so on. These and otherfactors are described in more detail above.

At 1506, a highest-scoring frame from the video segment is extractedbased on a comparison of the score of each frame of the video segmentwith the score of each other frame of the video segment to provide anextracted frame. The highest-scoring frame represents a “best” frame orhighest relative quality frame of the video segment. For instance, amongall the frames in the video segment, the highest-scoring frame may be aframe that is well-focused, appropriately zoomed, and has subjects thatare aligned for a group shot with their eyes open and with minimaloverlap between the subjects. At 1508, the extracted frame is transcodedas an image for display via a display device. At 1510, the calculating,extracting, and transcoding actions are performed for each segment ofthe plurality of segments. In this way, a best high quality image isextracted from each video segment of the video. This provides aplurality of high quality images, that are each extracted from adifferent portion of the video.

The above-described method constitutes an improvement over currentapproaches which use a primarily manual approach to extract images fromvideos. The automated nature of the described embodiments provides afast, efficient and easily scalable solution. That is, through the useof automated rules of the particular types discussed herein, highquality images can be more quickly and efficiently extracted andprovided to end users. For example, in scenarios in which people aregathered for a group photograph and a user captures the group byrecording video, the automated process can allow for quick and easyselection and extraction of high quality images from the video forupload to a social media site. As discussed above, a best high qualityimage is extracted from each different segment of the video. This wouldbe difficult if not impossible if the operations were to be performedmanually because manually identifying high quality images from videos isa slow and arduous process. Moreover, the automated rules promotescalability by removing the need for human intervention, such as addingadditional humans to perform the arduous manual process

Example System and Device

FIG. 16 illustrates an example system generally at 1600 that includes anexample computing device 1602 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe applications 108 and, in particular, the image extraction module110, which operates as described above. The computing device 1602 maybe, for example, a server of a service provider, a device associatedwith a client (e.g., a client device), an on-chip system, and/or anyother suitable computing device or computing system.

The example computing device 1602 is illustrated as including aprocessing system 1604, one or more computer-readable storage media1606, and one or more I/O interfaces 1608 that are communicativelycoupled, one to another. Although not shown, the computing device 1602may further include a system bus or other data and command transfersystem that couples the various components, one to another. A system buscan include any one or combination of different bus structures, such asa memory bus or memory controller, a peripheral bus, a universal serialbus, and/or a processor or local bus that utilizes any of a variety ofbus architectures. A variety of other examples are also contemplated,such as control and data lines.

The processing system 1604 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 1604 is illustrated as including hardware elements 1610 that maybe configured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 1610 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 1606 is illustrated as includingmemory/storage 1612. The memory/storage 1612 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 1612 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 1612 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable storage media 1606 may be configured in a variety ofother ways as further described below.

Input/output interface(s) 1608 are representative of functionality toallow a user to enter commands and information to computing device 1602,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 1602 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, elements, components, data structures, andso forth that perform particular tasks or implement particular abstractdata types. The terms “module,” “functionality,” and “component” as usedherein generally represent software, firmware, hardware, or acombination thereof. The features of the techniques described herein areplatform-independent, meaning that the techniques may be implemented ona variety of commercial computing platforms having a variety ofprocessors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 1602. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” refers to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media does not include signals per se orsignal bearing media. The computer-readable storage media includeshardware such as volatile and non-volatile, removable and non-removablemedia and/or storage devices implemented in a method or technologysuitable for storage of information such as computer readableinstructions, data structures, program modules, logic elements/circuits,or other data. Examples of computer-readable storage media may include,but are not limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disks (DVD) or other opticalstorage, hard disks, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or other storage device,tangible media, or article of manufacture suitable to store the desiredinformation and which may be accessed by a computer.

“Computer-readable signal media” refers to a signal-bearing medium thatis configured to transmit instructions to the hardware of the computingdevice 1602, such as via a network. Signal media typically may embodycomputer readable instructions, data structures, program modules, orother data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 1610 and computer-readablestorage media 1606 are representative of modules, programmable devicelogic and/or fixed device logic implemented in a hardware form that maybe employed in some implementations to implement at least some aspectsof the techniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 1610. The computing device 1602 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device1602 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements1610 of the processing system 1604. The instructions and/or functionsmay be executable/operable by one or more articles of manufacture (forexample, one or more computing devices 1602 and/or processing systems1604) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 1602 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 1614 via a platform 1616 as describedbelow.

The cloud 1614 includes and/or is representative of a platform 1616 forresources 1618. The platform 1616 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 1614. Theresources may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 1602. Resources can also include services providedover the Internet and/or through a subscriber network, such as acellular or Wi-Fi network.

The platform 1616 may abstract resources and functions to connect thecomputing device 1602 with other computing devices. The platform 1616may also serve to abstract scaling of resources to provide acorresponding level of scale to encountered demand for the resourcesthat are implemented via the platform 1616. Accordingly, in aninterconnected device implementation, implementation of functionalitydescribed herein may be distributed throughout the system 1600. Forexample, the functionality may be implemented in part on the computingdevice 1602 as well as via the platform 1616 that abstracts thefunctionality of the cloud 1614.

CONCLUSION

Various embodiments calculate a score for each frame of a video segmentbased on a plurality of subject-related factors associated with asubject captured in the frame relative to corresponding factors of thesubject in other frames of the video segment. A highest-scoring framefrom the video segment is then extracted based on a comparison of thescore of each frame of the video segment with the score of each otherframe of the video segment. Then, the extracted frame is transcoded asan image for display via a display device. The score calculation,extraction, and transcoding actions are performed automatically andwithout user intervention, which improves previous approaches that use aprimarily manual, tedious, and time consuming approach.

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. In a digital medium environment including atleast one computing device that supports image extraction from a video,an improved image extraction method comprising: calculating, by the atleast one computing device and without user intervention, a score foreach frame of a video segment based on a plurality of subject-relatedfactors associated with at least one subject captured in the framerelative to corresponding factors of the at least one subject capturedin other frames of the video segment, the score representing an overalllevel of image quality of the frame relative to the other frames in thevideo segment; automatically extracting, by the at least one computingdevice and without user intervention, a highest-scoring frame from thevideo segment based on a comparison of the score of each frame of thevideo segment to with the score of each other frame of the video segmentto provide an extracted frame; and transcoding, by the at least onecomputing device and without user intervention, the extracted frame asan image for display via a display device.
 2. A method as described inclaim 1, further comprising: segmenting the video into a plurality ofvideo segments based on at least one of a scene change or a face change;and performing the calculating, automatically extracting, andtranscoding actions for each video segment of the plurality of videosegments.
 3. A method as described in claim 1, wherein the at least onesubject includes multiple objects, and the plurality of subject-relatedfactors include a zoom factor indicating whether a magnification levelis changed to exclude at least one of the multiple objects in the frameor to include at least one additional object.
 4. A method as describedin claim 1, wherein the at least one subject includes multiple objects,and the plurality of subject-related factors includes an alignmentfactor representing a variance of distances between the multiple objectsin the frame.
 5. A method as described in claim 1, wherein the pluralityof subject-related factors includes an eyes factor indicating whetherthe at least one subject has eyes open, closed, or partially open.
 6. Amethod as described in claim 1, wherein the at least one subjectincludes multiple faces, and the plurality of subject-related factorsincludes an overlap factor indicating whether a first face of themultiple faces is at least partially obscured by a second face of themultiple faces in the frame.
 7. A method as described in claim 1,wherein the plurality of subject-related factors includes a motionfactor representing a level of motion associated with the at least onesubject in the frame, the level of motion indicating an amount of imageblur in the frame.
 8. A method as described in claim 1, wherein the atleast one subject includes multiple objects, and the plurality ofsubject-related factors includes an orientation factor indicating ameasure of divergence between orientations of the multiple objects inthe frame.
 9. In a digital medium environment that supports automaticextraction of images from a video, a system comprising an imageextraction module implemented at least partially in hardware of acomputing device, the image extraction module configured to: divide thevideo into a plurality of video segments based on at least one of scenechanges, face changes, or object changes; responsive to the video beingdivided into the plurality of video segments, automatically assign ascore for each frame of a respective video segment of the plurality ofvideo segments based on a plurality of subject-related factors of asubject captured in the frame relative to corresponding factors of thesubject captured in other frames of the respective video segment, thescore representing a level of image quality of the frame relative toother frames in the respective video segment; automatically extract oneframe from the respective video segment based on the score of the oneframe relative to scores assigned to the other frames in the respectivevideo segment; and automatically generate an image for display by atleast transcoding the one frame as the image for display via a displaydevice.
 10. A system as described in claim 9, wherein the subjectincludes multiple objects, and the plurality of subject-related factorsinclude a zoom factor indicating whether a magnification level is beingchanged to exclude at least one of the multiple objects in the frame orto include at least one additional object.
 11. A system as described inclaim 9, wherein the subject includes multiple objects, and theplurality of subject-related factors include an alignment factorrepresenting a variance of distances between the multiple objects in theframe.
 12. A system as described in claim 9, wherein the plurality ofsubject-related factors includes an eyes factor indicating whether thesubject has eyes open, closed, or partially open.
 13. A system asdescribed in claim 9, wherein the subject includes a group of faces, andthe plurality of subject-related factors include an overlap factorindicating whether a first face from the group of faces is at leastpartially obscured by a second face from the group of faces in theframe.
 14. A system as described in claim 9, wherein the plurality ofsubject-related factors includes a motion factor representing a level ofmotion associated with the subject in the frame, the level of motioncorresponding to an amount of image blur in the frame.
 15. A system asdescribed in claim 9, wherein the subject includes multiple objects, andthe plurality of subject-related factors include an orientation factorindicating a measure of divergence between orientations of the multipleobjects in the frame.
 16. In a digital medium environment that supportsimage extraction from a video, a method comprising: steps fordetermining a plurality of scores for each frame of a video segment,each score of the plurality of scores corresponding to a level of imagequality based on one or more factors associated with a subject capturedin the frame relative to one or more corresponding factors of thesubject captured in other frames of the video segment; steps forcalculating an overall score for each frame in the video segment basedon the plurality of scores for the frame, the overall score having avalue representing an overall level of image quality of the frame; stepsfor extracting one frame from the video segment based on the overallscore of the one frame; steps for generating an image from the video byat least transcoding the one frame as the image; and steps forinitiating a display of the image via a display device.
 17. A method asdescribed in claim 16, further comprising steps for dividing the videointo a plurality of video segments including the video segment, thevideo divided based on at least one a scene change, a change in a numberof faces in a scene, or a change in a number of objects in the scene.18. A method as described in claim 16, wherein the one or more factorsassociated with the subject captured in the frame include at least oneof: a zoom factor indicating whether a magnification level is beingchanged from the frame to a next frame in the video segment; or a motionfactor representing a level of activity associated with the subject inthe frame.
 19. A method as described in claim 16, wherein the subjectincludes a group of faces, and wherein the one or more factors includeat least one of: an alignment factor representing a variance ofdistances between the faces in the frame; an eyes factor indicatingwhether each of the faces has eyes open; or an overlap factor indicatingwhether a first face from the group of faces is at least partiallyobscured by a second face from the group of faces in the frame.
 20. Amethod as described in claim 16, wherein the extracted one frameincludes a highest relative overall score for the video segment andrepresents a highest relative overall level of image quality of theframe in comparison to other frames in the video segment.